Description Usage Arguments Value Author(s) References See Also Examples
Improve accuracy for learning algorithm to bond with a lot of weak classifiers to construct the only one strong classifier.
1 |
X |
input variable matrix to generate kernel. Data type must be a matrix format. |
Y |
output variable vector which will be declared as a matrix in SVM. Data type must be a matrix format. |
new.X |
test predictors. |
new.Y |
test response. |
c.n |
weighted term. |
B |
the number of iterations. |
kernel.type |
set an attributes of kernel using list(). kernel$type means a type of kernel, including 'linear', 'poly', and 'rbf'. kernel$par means a parameter of kernel. For example, par = degree for 'poly' and par = scale for 'rbf'. |
C |
regularization parameter. |
eps |
epsilon value. |
plotting |
logical values. If TRUE, plot the result. |
A function wsvm.boost generates a list consists of error.rate and predicted.model.
error.rate |
misclassification error rate |
predicted.model |
predicted model |
SungWhan Kim swiss747@korea.ac.kr
Soo-Heang Eo hanansh@korea.ac.kr
SungWhan Kim (2010). Weighted K-means SVM with Boosting algorithm for improving accuracy, Master Thesis, Korea University.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | # generate a simulation data set using mixture example(page 17, Friedman et al. 2000)
svm.data <- simul.wsvm(set.seeds = 123)
X <- svm.data$X
Y <- svm.data$Y
new.X <- svm.data$new.X
new.Y <- svm.data$new.Y
# run Weighted K-means clustering SVM with boosting algorithm
model <- wsvm(X, Y, c.n = rep(1/ length(Y),length(Y)))
# predict the model and compute an error rate.
pred <- wsvm.predict(X,Y, new.X, new.Y, model)
Error.rate(pred$predicted.Y, Y)
# add boost algorithm
boo <- wsvm.boost(X, Y, new.X, new.Y, c.n = rep(1 / length(Y),length(Y)),
B = 50, kernel.type = list(type = "rbf", par= 0.5), C = 4,
eps = 1e-10, plotting = TRUE)
boo
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